Overview

Dataset statistics

Number of variables8
Number of observations1519
Missing cells4173
Missing cells (%)34.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory95.1 KiB
Average record size in memory64.1 B

Variable types

DateTime1
Numeric7

Alerts

grocery_and_pharmacy is highly overall correlated with parks_change and 4 other fieldsHigh correlation
parks_change is highly overall correlated with grocery_and_pharmacy and 4 other fieldsHigh correlation
residential_change is highly overall correlated with grocery_and_pharmacy and 4 other fieldsHigh correlation
retail_and_recreation is highly overall correlated with grocery_and_pharmacy and 4 other fieldsHigh correlation
transit_stations is highly overall correlated with grocery_and_pharmacy and 4 other fieldsHigh correlation
workplaces is highly overall correlated with grocery_and_pharmacy and 4 other fieldsHigh correlation
daily_cases has 542 (35.7%) missing valuesMissing
retail_and_recreation has 545 (35.9%) missing valuesMissing
grocery_and_pharmacy has 545 (35.9%) missing valuesMissing
parks_change has 545 (35.9%) missing valuesMissing
transit_stations has 545 (35.9%) missing valuesMissing
workplaces has 906 (59.6%) missing valuesMissing
residential_change has 545 (35.9%) missing valuesMissing
Date has unique valuesUnique
daily_cases has 399 (26.3%) zerosZeros
grocery_and_pharmacy has 35 (2.3%) zerosZeros
residential_change has 24 (1.6%) zerosZeros

Reproduction

Analysis started2024-06-16 03:33:58.510651
Analysis finished2024-06-16 03:34:08.053233
Duration9.54 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Date
Date

UNIQUE 

Distinct1519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
Minimum2020-02-15 00:00:00
Maximum2024-04-12 00:00:00
2024-06-16T10:34:08.229165image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:08.480549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

daily_cases
Real number (ℝ)

MISSING  ZEROS 

Distinct357
Distinct (%)36.5%
Missing542
Missing (%)35.7%
Infinite0
Infinite (%)0.0%
Mean1672.7247
Minimum0
Maximum225694
Zeros399
Zeros (%)26.3%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-06-16T10:34:08.703292image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3239
95-th percentile3985.8
Maximum225694
Range225694
Interquartile range (IQR)239

Descriptive statistics

Standard deviation12218.728
Coefficient of variation (CV)7.3046855
Kurtosis260.2371
Mean1672.7247
Median Absolute Deviation (MAD)4
Skewness15.419289
Sum1634252
Variance1.4929731 × 108
MonotonicityNot monotonic
2024-06-16T10:34:08.903452image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 399
26.3%
1 49
 
3.2%
2 27
 
1.8%
3 12
 
0.8%
4 9
 
0.6%
6 9
 
0.6%
5 7
 
0.5%
8 7
 
0.5%
9 6
 
0.4%
14 5
 
0.3%
Other values (347) 447
29.4%
(Missing) 542
35.7%
ValueCountFrequency (%)
0 399
26.3%
1 49
 
3.2%
2 27
 
1.8%
3 12
 
0.8%
4 9
 
0.6%
5 7
 
0.5%
6 9
 
0.6%
7 5
 
0.3%
8 7
 
0.5%
9 6
 
0.4%
ValueCountFrequency (%)
225694 1
0.1%
211072 1
0.1%
189328 1
0.1%
40937 1
0.1%
32650 1
0.1%
32317 1
0.1%
31899 1
0.1%
31380 1
0.1%
30157 1
0.1%
29835 1
0.1%

retail_and_recreation
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)10.0%
Missing545
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean-25.291581
Minimum-87
Maximum21
Zeros13
Zeros (%)0.9%
Negative901
Negative (%)59.3%
Memory size12.0 KiB
2024-06-16T10:34:09.113875image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-87
5-th percentile-79
Q1-35.75
median-19.5
Q3-9
95-th percentile2
Maximum21
Range108
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation22.534277
Coefficient of variation (CV)-0.89097935
Kurtosis0.6320948
Mean-25.291581
Median Absolute Deviation (MAD)12.5
Skewness-1.0586386
Sum-24634
Variance507.79362
MonotonicityNot monotonic
2024-06-16T10:34:09.380437image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 36
 
2.4%
-7 32
 
2.1%
-8 30
 
2.0%
-21 29
 
1.9%
-15 29
 
1.9%
-12 28
 
1.8%
-13 26
 
1.7%
-16 23
 
1.5%
-10 23
 
1.5%
-9 23
 
1.5%
Other values (87) 695
45.8%
(Missing) 545
35.9%
ValueCountFrequency (%)
-87 1
 
0.1%
-86 5
 
0.3%
-85 6
 
0.4%
-84 16
1.1%
-83 5
 
0.3%
-82 5
 
0.3%
-81 7
0.5%
-80 3
 
0.2%
-79 6
 
0.4%
-78 5
 
0.3%
ValueCountFrequency (%)
21 1
 
0.1%
19 2
 
0.1%
13 2
 
0.1%
10 4
0.3%
9 2
 
0.1%
8 4
0.3%
7 3
0.2%
6 6
0.4%
5 6
0.4%
4 6
0.4%

grocery_and_pharmacy
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct87
Distinct (%)8.9%
Missing545
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean-9.9507187
Minimum-67
Maximum22
Zeros35
Zeros (%)2.3%
Negative634
Negative (%)41.7%
Memory size12.0 KiB
2024-06-16T10:34:09.586561image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-67
5-th percentile-57
Q1-17
median-5
Q32
95-th percentile12
Maximum22
Range89
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.894678
Coefficient of variation (CV)-1.8988255
Kurtosis1.439318
Mean-9.9507187
Median Absolute Deviation (MAD)9
Skewness-1.3313865
Sum-9692
Variance357.00887
MonotonicityNot monotonic
2024-06-16T10:34:09.783539image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 37
 
2.4%
-1 36
 
2.4%
0 35
 
2.3%
1 34
 
2.2%
-9 32
 
2.1%
-5 31
 
2.0%
4 30
 
2.0%
-8 29
 
1.9%
-7 29
 
1.9%
-3 29
 
1.9%
Other values (77) 652
42.9%
(Missing) 545
35.9%
ValueCountFrequency (%)
-67 1
 
0.1%
-66 7
0.5%
-65 3
 
0.2%
-64 7
0.5%
-63 4
 
0.3%
-62 10
0.7%
-61 6
0.4%
-60 2
 
0.1%
-59 2
 
0.1%
-58 4
 
0.3%
ValueCountFrequency (%)
22 2
 
0.1%
21 1
 
0.1%
20 2
 
0.1%
19 4
0.3%
18 3
 
0.2%
17 4
0.3%
16 4
0.3%
15 8
0.5%
14 7
0.5%
13 7
0.5%

parks_change
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)11.5%
Missing545
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean-22.379877
Minimum-83
Maximum38
Zeros15
Zeros (%)1.0%
Negative846
Negative (%)55.7%
Memory size12.0 KiB
2024-06-16T10:34:09.985668image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-83
5-th percentile-72.35
Q1-33
median-19
Q3-8
95-th percentile12
Maximum38
Range121
Interquartile range (IQR)25

Descriptive statistics

Standard deviation22.425197
Coefficient of variation (CV)-1.002025
Kurtosis0.51102019
Mean-22.379877
Median Absolute Deviation (MAD)13
Skewness-0.61912227
Sum-21798
Variance502.88946
MonotonicityNot monotonic
2024-06-16T10:34:10.220140image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-17 30
 
2.0%
-9 29
 
1.9%
-31 27
 
1.8%
-15 26
 
1.7%
-14 26
 
1.7%
-30 24
 
1.6%
-18 24
 
1.6%
-10 23
 
1.5%
-5 22
 
1.4%
-16 21
 
1.4%
Other values (102) 722
47.5%
(Missing) 545
35.9%
ValueCountFrequency (%)
-83 1
 
0.1%
-82 1
 
0.1%
-81 2
 
0.1%
-80 11
0.7%
-79 12
0.8%
-78 7
0.5%
-77 4
 
0.3%
-76 3
 
0.2%
-75 2
 
0.1%
-74 3
 
0.2%
ValueCountFrequency (%)
38 1
 
0.1%
36 1
 
0.1%
32 1
 
0.1%
31 1
 
0.1%
29 1
 
0.1%
24 1
 
0.1%
23 2
0.1%
22 4
0.3%
21 2
0.1%
20 1
 
0.1%

transit_stations
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)11.8%
Missing545
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean-30.336756
Minimum-89
Maximum56
Zeros11
Zeros (%)0.7%
Negative881
Negative (%)58.0%
Memory size12.0 KiB
2024-06-16T10:34:10.410402image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile-83
Q1-52
median-24
Q3-9
95-th percentile4
Maximum56
Range145
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.621583
Coefficient of variation (CV)-0.8775356
Kurtosis-0.63751836
Mean-30.336756
Median Absolute Deviation (MAD)20
Skewness-0.36279596
Sum-29548
Variance708.70868
MonotonicityNot monotonic
2024-06-16T10:34:10.620446image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18 28
 
1.8%
-7 26
 
1.7%
-55 25
 
1.6%
-5 23
 
1.5%
-3 22
 
1.4%
-4 22
 
1.4%
-10 21
 
1.4%
-6 20
 
1.3%
-12 20
 
1.3%
-20 20
 
1.3%
Other values (105) 747
49.2%
(Missing) 545
35.9%
ValueCountFrequency (%)
-89 4
 
0.3%
-88 5
 
0.3%
-87 13
0.9%
-86 11
0.7%
-85 4
 
0.3%
-84 7
0.5%
-83 7
0.5%
-82 1
 
0.1%
-81 1
 
0.1%
-80 1
 
0.1%
ValueCountFrequency (%)
56 1
 
0.1%
53 1
 
0.1%
26 1
 
0.1%
25 1
 
0.1%
22 1
 
0.1%
20 1
 
0.1%
19 1
 
0.1%
18 1
 
0.1%
17 3
0.2%
16 2
0.1%

workplaces
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)16.5%
Missing906
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean-8.0261011
Minimum-84
Maximum30
Zeros12
Zeros (%)0.8%
Negative339
Negative (%)22.3%
Memory size12.0 KiB
2024-06-16T10:34:11.061514image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-84
5-th percentile-62
Q1-17
median-2
Q310
95-th percentile24
Maximum30
Range114
Interquartile range (IQR)27

Descriptive statistics

Standard deviation25.346797
Coefficient of variation (CV)-3.158046
Kurtosis0.17826171
Mean-8.0261011
Median Absolute Deviation (MAD)13
Skewness-0.98266616
Sum-4920
Variance642.4601
MonotonicityNot monotonic
2024-06-16T10:34:11.297673image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2 24
 
1.6%
-5 17
 
1.1%
-1 17
 
1.1%
-4 17
 
1.1%
6 16
 
1.1%
-7 14
 
0.9%
-3 14
 
0.9%
3 14
 
0.9%
-8 14
 
0.9%
4 13
 
0.9%
Other values (91) 453
29.8%
(Missing) 906
59.6%
ValueCountFrequency (%)
-84 1
0.1%
-82 1
0.1%
-80 1
0.1%
-76 1
0.1%
-74 1
0.1%
-73 1
0.1%
-71 1
0.1%
-70 2
0.1%
-69 1
0.1%
-67 1
0.1%
ValueCountFrequency (%)
30 2
 
0.1%
29 3
 
0.2%
28 6
0.4%
27 3
 
0.2%
26 5
0.3%
25 5
0.3%
24 8
0.5%
23 5
0.3%
22 4
0.3%
21 8
0.5%

residential_change
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct50
Distinct (%)5.1%
Missing545
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean7.3285421
Minimum-13
Maximum43
Zeros24
Zeros (%)1.6%
Negative139
Negative (%)9.2%
Memory size12.0 KiB
2024-06-16T10:34:11.490465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile-4
Q13
median6
Q39
95-th percentile31
Maximum43
Range56
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.9660125
Coefficient of variation (CV)1.2234374
Kurtosis2.6782074
Mean7.3285421
Median Absolute Deviation (MAD)3
Skewness1.5249703
Sum7138
Variance80.38938
MonotonicityNot monotonic
2024-06-16T10:34:11.690216image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 101
 
6.6%
7 91
 
6.0%
5 88
 
5.8%
4 76
 
5.0%
8 51
 
3.4%
3 44
 
2.9%
2 43
 
2.8%
9 42
 
2.8%
10 41
 
2.7%
1 37
 
2.4%
Other values (40) 360
23.7%
(Missing) 545
35.9%
ValueCountFrequency (%)
-13 1
 
0.1%
-8 5
 
0.3%
-7 5
 
0.3%
-6 7
 
0.5%
-5 21
1.4%
-4 22
1.4%
-3 26
1.7%
-2 29
1.9%
-1 23
1.5%
0 24
1.6%
ValueCountFrequency (%)
43 1
 
0.1%
39 1
 
0.1%
38 2
 
0.1%
37 3
 
0.2%
36 8
0.5%
35 9
0.6%
34 7
0.5%
33 3
 
0.2%
32 8
0.5%
31 9
0.6%

Interactions

2024-06-16T10:34:05.731482image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:58.890686image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:59.960411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:01.053961image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:02.400331image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:03.630183image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:04.735152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:05.890410image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:59.060406image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:00.123362image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:01.230544image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:02.586334image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:03.790047image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:04.864255image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:06.140401image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:59.237005image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:00.260247image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:01.395593image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:02.753804image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:03.998476image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:05.012600image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:06.400051image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:59.386289image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:00.410422image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:01.556595image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:02.902821image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:04.136103image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:05.140943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:06.601149image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:59.540377image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:00.560475image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:01.770505image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:03.085259image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:04.296179image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:05.270115image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:06.890101image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:59.690421image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:00.710322image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:01.970462image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:03.300370image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:04.461251image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:05.420589image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:07.071557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:33:59.833285image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:00.910432image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:02.162871image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:03.460335image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:04.590222image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-16T10:34:05.567214image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-06-16T10:34:11.810428image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
daily_casesgrocery_and_pharmacyparks_changeresidential_changeretail_and_recreationtransit_stationsworkplaces
daily_cases1.000-0.193-0.0160.313-0.223-0.427-0.406
grocery_and_pharmacy-0.1931.0000.795-0.6570.7570.7450.625
parks_change-0.0160.7951.000-0.5630.8160.6800.660
residential_change0.313-0.657-0.5631.000-0.740-0.795-0.751
retail_and_recreation-0.2230.7570.816-0.7401.0000.8740.775
transit_stations-0.4270.7450.680-0.7950.8741.0000.811
workplaces-0.4060.6250.660-0.7510.7750.8111.000

Missing values

2024-06-16T10:34:07.270343image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-16T10:34:07.510285image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-16T10:34:07.840511image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Datedaily_casesretail_and_recreationgrocery_and_pharmacyparks_changetransit_stationsworkplacesresidential_change
02020-02-15NaN-8.0-4.0-5.0-14.0-4.07.0
12020-02-16NaN-15.0-9.0-11.0-20.0-5.08.0
22020-02-17NaN-16.0-12.0-10.0-19.03.07.0
32020-02-18NaN-14.0-9.0-9.0-18.010.08.0
42020-02-19NaN-8.0-16.0-15.0-21.09.06.0
52020-02-20NaN-2.0-18.0-17.0-5.06.04.0
62020-02-21NaN-14.0-9.0-14.0-18.0-2.07.0
72020-02-22NaN-8.00.0-3.0-13.0-2.07.0
82020-02-23NaN-13.0-5.00.0-18.0-2.06.0
92020-02-24NaN-14.0-12.0-5.0-18.04.06.0
Datedaily_casesretail_and_recreationgrocery_and_pharmacyparks_changetransit_stationsworkplacesresidential_change
15092024-04-03NaNNaNNaNNaNNaNNaNNaN
15102024-04-04NaNNaNNaNNaNNaNNaNNaN
15112024-04-05NaNNaNNaNNaNNaNNaNNaN
15122024-04-06NaNNaNNaNNaNNaNNaNNaN
15132024-04-07NaNNaNNaNNaNNaNNaNNaN
15142024-04-08NaNNaNNaNNaNNaNNaNNaN
15152024-04-09NaNNaNNaNNaNNaNNaNNaN
15162024-04-10NaNNaNNaNNaNNaNNaNNaN
15172024-04-11NaNNaNNaNNaNNaNNaNNaN
15182024-04-12NaNNaNNaNNaNNaNNaNNaN